Annotations

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%load_ext autoreload
%autoreload 2

import os

import dimcat as dc
import ms3
import plotly.express as px
from dimcat import groupers, plotting

import utils

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RESULTS_PATH = os.path.abspath(os.path.join(utils.OUTPUT_FOLDER, "overview"))
os.makedirs(RESULTS_PATH, exist_ok=True)


def make_output_path(
    filename: str,
    extension=None,
    path=RESULTS_PATH,
) -> str:
    return utils.make_output_path(filename=filename, extension=extension, path=path)


def save_figure_as(
    fig, filename, formats=("png", "pdf"), directory=RESULTS_PATH, **kwargs
):
    if formats is not None:
        for fmt in formats:
            plotting.write_image(fig, filename, directory, format=fmt, **kwargs)
    else:
        plotting.write_image(fig, filename, directory, **kwargs)

Loading data

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D = utils.get_dataset("poulenc_mouvements_perpetuels", corpus_release="v2.4")
package = D.inputs.get_package()
package_info = package._package.custom
git_tag = package_info.get("git_tag")
utils.print_heading("Data and software versions")
print("Francis Poulenc – Mouvements Perpetuels version v2.4")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D

Data and software versions
--------------------------

Francis Poulenc – Mouvements Perpetuels version v2.4
Datapackage 'poulenc_mouvements_perpetuels' @ v2.4
dimcat version 3.4.0

Dataset
=======
{'inputs': {'basepath': None,
            'packages': {'poulenc_mouvements_perpetuels': ["'poulenc_mouvements_perpetuels.measures' "
                                                           '(MuseScoreFacetName.MuseScoreMeasures)',
                                                           "'poulenc_mouvements_perpetuels.notes' "
                                                           '(MuseScoreFacetName.MuseScoreNotes)',
                                                           "'poulenc_mouvements_perpetuels.expanded' "
                                                           '(MuseScoreFacetName.MuseScoreHarmonies)',
                                                           "'poulenc_mouvements_perpetuels.chords' "
                                                           '(MuseScoreFacetName.MuseScoreChords)',
                                                           "'poulenc_mouvements_perpetuels.metadata' "
                                                           '(FeatureName.Metadata)']}},
 'outputs': {'basepath': None, 'packages': {}},
 'pipeline': []}

filtered_D = D.apply_step("HasHarmonyLabelsFilter")
all_metadata = filtered_D.get_metadata()

assert len(all_metadata) > 0, "No pieces selected for analysis."
chronological_corpus_names = all_metadata.get_corpus_names()

DCML harmony labels

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all_annotations = filtered_D.get_feature("DcmlAnnotations")
is_annotated_mask = all_metadata.label_count > 0
is_annotated_index = all_metadata.index[is_annotated_mask]
annotated_notes = filtered_D.get_feature("notes").subselect(is_annotated_index)
print(f"The annotated pieces have {len(annotated_notes)} notes.")

The annotated pieces have 1563 notes.

all_chords = filtered_D.get_feature("harmonylabels")
print(
    f"{len(all_annotations)} annotations, of which {len(all_chords)} are harmony labels."
)

277 annotations, of which 268 are harmony labels.

Harmony labels

Unigrams

For computing unigram statistics, the tokens need to be grouped by their occurrence within a major or a minor key because this changes their meaning. To that aim, the annotated corpus needs to be sliced into contiguous localkey segments which are then grouped into a major (is_minor=False) and a minor group.

root_durations = (
    all_chords[all_chords.root.between(-5, 6)]
    .groupby(["root", "chord_type"])
    .duration_qb.sum()
)
# sort by stacked bar length:
# root_durations = root_durations.sort_values(key=lambda S: S.index.get_level_values(0).map(S.groupby(level=0).sum()),
# ascending=False)
bar_data = root_durations.reset_index()
bar_data.root = bar_data.root.map(ms3.fifths2iv)
fig = px.bar(
    bar_data,
    x="root",
    y="duration_qb",
    color="chord_type",
    title="Distribution of chord types over chord roots",
    labels=dict(
        root="Chord root expressed as interval above the local (or secondary) tonic",
        duration_qb="duration in quarter notes",
        chord_type="chord type",
    ),
)
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "chord_type_distribution_over_scale_degrees_absolute_stacked_bars")
fig.show()

relative_roots = all_chords[
    ["numeral", "duration_qb", "relativeroot", "localkey_is_minor", "chord_type"]
].copy()
relative_roots["relativeroot_resolved"] = ms3.transform(
    relative_roots, ms3.resolve_relative_keys, ["relativeroot", "localkey_is_minor"]
)
has_rel = relative_roots.relativeroot_resolved.notna()
relative_roots.loc[has_rel, "localkey_is_minor"] = relative_roots.loc[
    has_rel, "relativeroot_resolved"
].str.islower()
relative_roots["root"] = ms3.transform(
    relative_roots, ms3.roman_numeral2fifths, ["numeral", "localkey_is_minor"]
)
chord_type_frequency = all_chords.chord_type.value_counts()
replace_rare = ms3.map_dict(
    {t: "other" for t in chord_type_frequency[chord_type_frequency < 500].index}
)
relative_roots["type_reduced"] = relative_roots.chord_type.map(replace_rare)
# is_special = relative_roots.chord_type.isin(('It', 'Ger', 'Fr'))
# relative_roots.loc[is_special, 'root'] = -4

root_durations = (
    relative_roots.groupby(["root", "type_reduced"])
    .duration_qb.sum()
    .sort_values(ascending=False)
)
bar_data = root_durations.reset_index()
bar_data.root = bar_data.root.map(ms3.fifths2iv)
root_order = (
    bar_data.groupby("root")
    .duration_qb.sum()
    .sort_values(ascending=False)
    .index.to_list()
)
fig = px.bar(
    bar_data,
    x="root",
    y="duration_qb",
    color="type_reduced",
    barmode="group",
    log_y=True,
    color_discrete_map=utils.TYPE_COLORS,
    category_orders=dict(
        root=root_order,
        type_reduced=relative_roots.type_reduced.value_counts().index.to_list(),
    ),
    labels=dict(
        root="intervallic difference between chord root to the local or secondary tonic",
        duration_qb="duration in quarter notes",
        type_reduced="chord type",
    ),
    width=1000,
    height=400,
)
fig.update_layout(
    **utils.STD_LAYOUT,
    legend=dict(
        orientation="h",
        xanchor="right",
        x=1,
        y=1,
    ),
)
save_figure_as(fig, "chord_type_distribution_over_scale_degrees_absolute_grouped_bars")
fig.show()

print(
    f"Reduced to {len(set(bar_data.iloc[:,:2].itertuples(index=False, name=None)))} types. "
    f"Paper cites the sum of types in major and types in minor (see below), treating them as distinct."
)

Reduced to 6 types. Paper cites the sum of types in major and types in minor (see below), treating them as distinct.

dim_or_aug = bar_data[
    bar_data.root.str.startswith("a") | bar_data.root.str.startswith("d")
].duration_qb.sum()
complete = bar_data.duration_qb.sum()
print(
    f"On diminished or augmented scale degrees: {dim_or_aug} / {complete} = {dim_or_aug / complete}"
)

On diminished or augmented scale degrees: 0.0 / 313.875 = 0.0

chords_by_mode = groupers.ModeGrouper().process(all_chords)
chords_by_mode.format = "scale_degree"

Whole dataset

unigram_proportions = chords_by_mode.get_default_analysis()
unigram_proportions.make_ranking_table()

mode	major					minor
	chord_and_mode	scale_degrees	duration_qb	proportion	proportion_%	chord_and_mode	scale_degrees	duration_qb	proportion	proportion_%
rank
1	ii7(9), major	(2, 4, 6, 1)	20.5	0.080550	8.06 %	iv(96), minor	(4, 6, 2)	7.000	0.117895	11.79 %
2	I, major	(1, 3, 5)	18.0	0.070727	7.07 %	i2, minor	(7, 1, 3, 5)	5.000	0.084211	8.42 %
3	iii(b9), major	(3, 5, 7)	16.0	0.062868	6.29 %	i, minor	(1, 3, 5)	5.000	0.084211	8.42 %
4	IVM2, major	(3, 4, 6, 1)	14.0	0.055010	5.5 %	i64(13), minor	(5, 1, 3)	4.000	0.067368	6.74 %
5	IM7, major	(1, 3, 5, 7)	13.0	0.051081	5.11 %	v, minor	(5, 7, 2)	4.000	0.067368	6.74 %
6	V(^2), major	(5, 6, 2)	11.0	0.043222	4.32 %	ii%65, minor	(4, 6, 1, 2)	4.000	0.067368	6.74 %
7	V7(^2), major	(5, 6, 2, 4)	9.0	0.035363	3.54 %	i(9b6), minor	(1, 3, b6)	4.000	0.067368	6.74 %
8	V(96), major	(5, 7, 3)	8.0	0.031434	3.14 %	ii%43, minor	(6, 1, 2, 4)	4.000	0.067368	6.74 %
9	ii7, major	(2, 4, 6, 1)	8.0	0.031434	3.14 %	i43, minor	(5, 7, 1, 3)	3.000	0.050526	5.05 %
10	V(6), major	(5, 7, 3)	7.0	0.027505	2.75 %	V7(9b6)/III, minor	(7, 2, b5, 6)	2.375	0.040000	4.0 %
11	ii, major	(2, 4, 6)	6.5	0.025540	2.55 %	iio6, minor	(4, 6, 2)	2.000	0.033684	3.37 %
12	IM7(9), major	(1, 3, 5, 7)	6.0	0.023576	2.36 %	V(9)/III, minor	(7, 2, 4)	2.000	0.033684	3.37 %
13	i7, major	(1, b3, 5, b7)	6.0	0.023576	2.36 %	V(96)/III, minor	(7, 2, 5)	2.000	0.033684	3.37 %
14	V(6+4), major	(5, 7, 3)	6.0	0.023576	2.36 %	i(96), minor	(1, 3, 6)	2.000	0.033684	3.37 %
15	v(119), major	(5, b7, 2)	5.0	0.019646	1.96 %	i(9), minor	(1, 3, 5)	2.000	0.033684	3.37 %
16	V7(96), major	(5, 7, 3, 4)	4.5	0.017682	1.77 %	i(6), minor	(1, 3, 6)	2.000	0.033684	3.37 %
17	ii65(9), major	(4, 6, 1, 2)	4.0	0.015717	1.57 %	V7/iv, minor	(1, #3, 5, 7)	2.000	0.033684	3.37 %
18	v7(b62), major	(6, b7, b3, 4)	4.0	0.015717	1.57 %	v6, minor	(7, 2, 5)	2.000	0.033684	3.37 %
19	V, major	(5, 7, 2)	4.0	0.015717	1.57 %	iv, minor	(4, 6, 1)	1.000	0.016842	1.68 %
20	V7(+964), major	(5, 1, 3, 4)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
21	V7(9^2), major	(5, 6, 2, 4)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
22	iM7(^4), major	(1, b3, 4, 7)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
23	IV, major	(4, 6, 1)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
24	I+7(#9), major	(1, 3, #5, b7)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
25	V7/V, major	(2, #4, 6, 1)	3.5	0.013752	1.38 %	NaN	NaN	NaN	NaN	NaN
26	ii(9), major	(2, 4, 6)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
27	V7(9)/V, major	(2, #4, 6, 1)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
28	i, major	(1, b3, 5)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
29	ii(139), major	(2, 4, 6)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
30	ii6(119), major	(4, 6, 2)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
31	V(6^2), major	(5, 6, 3)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
32	V(64), major	(5, 1, 3)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
33	IVM65, major	(6, 1, 3, 4)	2.5	0.009823	0.98 %	NaN	NaN	NaN	NaN	NaN
34	V7, major	(5, 7, 2, 4)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
35	VM7(^2), major	(5, 6, 2, #4)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
36	iM7(4), major	(1, 4, 5, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
37	iM7, major	(1, b3, 5, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
38	iii7(b9), major	(3, 5, 7, 2)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
39	ii(64+2), major	(2, 5, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
40	v(b96^2), major	(5, 6, 3)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
41	v(#119^2), major	(5, 6, 2)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
42	V7(6), major	(5, 7, 3, 4)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
43	V(b6^2), major	(5, 6, b3)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
44	V(b139), major	(5, 7, 2)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
45	ii7(+2), major	(2, 4, 6, 1)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
46	ii(94), major	(2, 5, 6)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
47	IM7(6), major	(1, 3, 6, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
48	ii(13), major	(2, 4, 6)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
49	V7(964), major	(5, 1, 3, 4)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
50	i(b13), major	(1, b3, 5)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
51	ii2, major	(1, 2, 4, 6)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
52	I(^2), major	(1, 2, 5)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
53	iii64, major	(7, 3, 5)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
54	ii64(9), major	(6, 2, 4)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
55	iii, major	(3, 5, 7)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
56	iii43/V, major	(#4, 6, 7, 2)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
57	IV64(9), major	(1, 4, 6)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
58	I64(9), major	(5, 1, 3)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
59	vi, major	(6, 1, 3)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN

chords_by_mode.apply_step("Counter")

					count
mode	corpus	piece	chord_and_mode	scale_degrees
major	poulenc_mouvements_perpetuels	01_assez_modere	IM7, major	(1, 3, 5, 7)	13
			I, major	(1, 3, 5)	12
			V(^2), major	(5, 6, 2)	11
			V7(^2), major	(5, 6, 2, 4)	9
			i7, major	(1, b3, 5, b7)	6
...	...	...	...	...	...
minor	poulenc_mouvements_perpetuels	02_tres_modere	i(96), minor	(1, 3, 6)	2
			iio6, minor	(4, 6, 2)	2
			v6, minor	(7, 2, 5)	2
			iv, minor	(4, 6, 1)	1
			V7(9b6)/III, minor	(7, 2, b5, 6)	1

79 rows × 1 columns

chords_by_mode.format = "scale_degree"
chords_by_mode.get_default_analysis().make_ranking_table()

mode	major					minor
	chord_and_mode	scale_degrees	duration_qb	proportion	proportion_%	chord_and_mode	scale_degrees	duration_qb	proportion	proportion_%
rank
1	ii7(9), major	(2, 4, 6, 1)	20.5	0.080550	8.06 %	iv(96), minor	(4, 6, 2)	7.000	0.117895	11.79 %
2	I, major	(1, 3, 5)	18.0	0.070727	7.07 %	i2, minor	(7, 1, 3, 5)	5.000	0.084211	8.42 %
3	iii(b9), major	(3, 5, 7)	16.0	0.062868	6.29 %	i, minor	(1, 3, 5)	5.000	0.084211	8.42 %
4	IVM2, major	(3, 4, 6, 1)	14.0	0.055010	5.5 %	i64(13), minor	(5, 1, 3)	4.000	0.067368	6.74 %
5	IM7, major	(1, 3, 5, 7)	13.0	0.051081	5.11 %	v, minor	(5, 7, 2)	4.000	0.067368	6.74 %
6	V(^2), major	(5, 6, 2)	11.0	0.043222	4.32 %	ii%65, minor	(4, 6, 1, 2)	4.000	0.067368	6.74 %
7	V7(^2), major	(5, 6, 2, 4)	9.0	0.035363	3.54 %	i(9b6), minor	(1, 3, b6)	4.000	0.067368	6.74 %
8	V(96), major	(5, 7, 3)	8.0	0.031434	3.14 %	ii%43, minor	(6, 1, 2, 4)	4.000	0.067368	6.74 %
9	ii7, major	(2, 4, 6, 1)	8.0	0.031434	3.14 %	i43, minor	(5, 7, 1, 3)	3.000	0.050526	5.05 %
10	V(6), major	(5, 7, 3)	7.0	0.027505	2.75 %	V7(9b6)/III, minor	(7, 2, b5, 6)	2.375	0.040000	4.0 %
11	ii, major	(2, 4, 6)	6.5	0.025540	2.55 %	iio6, minor	(4, 6, 2)	2.000	0.033684	3.37 %
12	IM7(9), major	(1, 3, 5, 7)	6.0	0.023576	2.36 %	V(9)/III, minor	(7, 2, 4)	2.000	0.033684	3.37 %
13	i7, major	(1, b3, 5, b7)	6.0	0.023576	2.36 %	V(96)/III, minor	(7, 2, 5)	2.000	0.033684	3.37 %
14	V(6+4), major	(5, 7, 3)	6.0	0.023576	2.36 %	i(96), minor	(1, 3, 6)	2.000	0.033684	3.37 %
15	v(119), major	(5, b7, 2)	5.0	0.019646	1.96 %	i(9), minor	(1, 3, 5)	2.000	0.033684	3.37 %
16	V7(96), major	(5, 7, 3, 4)	4.5	0.017682	1.77 %	i(6), minor	(1, 3, 6)	2.000	0.033684	3.37 %
17	ii65(9), major	(4, 6, 1, 2)	4.0	0.015717	1.57 %	V7/iv, minor	(1, #3, 5, 7)	2.000	0.033684	3.37 %
18	v7(b62), major	(6, b7, b3, 4)	4.0	0.015717	1.57 %	v6, minor	(7, 2, 5)	2.000	0.033684	3.37 %
19	V, major	(5, 7, 2)	4.0	0.015717	1.57 %	iv, minor	(4, 6, 1)	1.000	0.016842	1.68 %
20	V7(+964), major	(5, 1, 3, 4)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
21	V7(9^2), major	(5, 6, 2, 4)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
22	iM7(^4), major	(1, b3, 4, 7)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
23	IV, major	(4, 6, 1)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
24	I+7(#9), major	(1, 3, #5, b7)	4.0	0.015717	1.57 %	NaN	NaN	NaN	NaN	NaN
25	V7/V, major	(2, #4, 6, 1)	3.5	0.013752	1.38 %	NaN	NaN	NaN	NaN	NaN
26	ii(9), major	(2, 4, 6)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
27	V7(9)/V, major	(2, #4, 6, 1)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
28	i, major	(1, b3, 5)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
29	ii(139), major	(2, 4, 6)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
30	ii6(119), major	(4, 6, 2)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
31	V(6^2), major	(5, 6, 3)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
32	V(64), major	(5, 1, 3)	3.0	0.011788	1.18 %	NaN	NaN	NaN	NaN	NaN
33	IVM65, major	(6, 1, 3, 4)	2.5	0.009823	0.98 %	NaN	NaN	NaN	NaN	NaN
34	V7, major	(5, 7, 2, 4)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
35	VM7(^2), major	(5, 6, 2, #4)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
36	iM7(4), major	(1, 4, 5, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
37	iM7, major	(1, b3, 5, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
38	iii7(b9), major	(3, 5, 7, 2)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
39	ii(64+2), major	(2, 5, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
40	v(b96^2), major	(5, 6, 3)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
41	v(#119^2), major	(5, 6, 2)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
42	V7(6), major	(5, 7, 3, 4)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
43	V(b6^2), major	(5, 6, b3)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
44	V(b139), major	(5, 7, 2)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
45	ii7(+2), major	(2, 4, 6, 1)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
46	ii(94), major	(2, 5, 6)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
47	IM7(6), major	(1, 3, 6, 7)	2.0	0.007859	0.79 %	NaN	NaN	NaN	NaN	NaN
48	ii(13), major	(2, 4, 6)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
49	V7(964), major	(5, 1, 3, 4)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
50	i(b13), major	(1, b3, 5)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
51	ii2, major	(1, 2, 4, 6)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
52	I(^2), major	(1, 2, 5)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
53	iii64, major	(7, 3, 5)	1.0	0.003929	0.39 %	NaN	NaN	NaN	NaN	NaN
54	ii64(9), major	(6, 2, 4)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
55	iii, major	(3, 5, 7)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
56	iii43/V, major	(#4, 6, 7, 2)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
57	IV64(9), major	(1, 4, 6)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
58	I64(9), major	(5, 1, 3)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN
59	vi, major	(6, 1, 3)	0.5	0.001965	0.2 %	NaN	NaN	NaN	NaN	NaN

unigram_proportions.plot_grouped()